Overview

Dataset statistics

Number of variables17
Number of observations13349
Missing cells3952
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory191.4 B

Variable types

Numeric12
Categorical5

Alerts

Year has constant value "2019" Constant
Duration has a high cardinality: 373 distinct values High cardinality
Source is highly correlated with DestinationHigh correlation
Destination is highly correlated with SourceHigh correlation
Total_Stops is highly correlated with Price and 1 other fieldsHigh correlation
Price is highly correlated with Total_Stops and 1 other fieldsHigh correlation
duration_hour is highly correlated with Total_Stops and 1 other fieldsHigh correlation
Source is highly correlated with DestinationHigh correlation
Destination is highly correlated with SourceHigh correlation
Total_Stops is highly correlated with Price and 1 other fieldsHigh correlation
Price is highly correlated with Total_Stops and 1 other fieldsHigh correlation
duration_hour is highly correlated with Total_Stops and 1 other fieldsHigh correlation
Source is highly correlated with DestinationHigh correlation
Destination is highly correlated with SourceHigh correlation
Total_Stops is highly correlated with Price and 1 other fieldsHigh correlation
Price is highly correlated with Total_Stops and 1 other fieldsHigh correlation
duration_hour is highly correlated with Total_Stops and 1 other fieldsHigh correlation
Source is highly correlated with YearHigh correlation
Month is highly correlated with YearHigh correlation
Total_Stops is highly correlated with YearHigh correlation
Year is highly correlated with Source and 2 other fieldsHigh correlation
Airline is highly correlated with Source and 6 other fieldsHigh correlation
Source is highly correlated with Airline and 4 other fieldsHigh correlation
Destination is highly correlated with Source and 3 other fieldsHigh correlation
Total_Stops is highly correlated with Airline and 3 other fieldsHigh correlation
Additional_Info is highly correlated with Airline and 1 other fieldsHigh correlation
Price is highly correlated with Airline and 1 other fieldsHigh correlation
Month is highly correlated with DestinationHigh correlation
Arrival_hour is highly correlated with Airline and 2 other fieldsHigh correlation
Arrival_min is highly correlated with Airline and 1 other fieldsHigh correlation
Dept_hour is highly correlated with Arrival_hourHigh correlation
duration_hour is highly correlated with Airline and 3 other fieldsHigh correlation
duration_min is highly correlated with SourceHigh correlation
Price has 2669 (20.0%) missing values Missing
duration_min has 1282 (9.6%) missing values Missing
Airline has 405 (3.0%) zeros Zeros
Destination has 3581 (26.8%) zeros Zeros
Arrival_hour has 411 (3.1%) zeros Zeros
Arrival_min has 1827 (13.7%) zeros Zeros
Dept_min has 2590 (19.4%) zeros Zeros

Reproduction

Analysis started2022-04-11 08:41:45.420689
Analysis finished2022-04-11 08:42:38.663796
Duration53.24 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

Distinct10680
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4540.556596
Minimum0
Maximum10682
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2022-04-11T14:12:39.138249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile334.4
Q11669
median4007
Q37345
95-th percentile10014.6
Maximum10682
Range10682
Interquartile range (IQR)5676

Descriptive statistics

Standard deviation3208.701447
Coefficient of variation (CV)0.7066757962
Kurtosis-1.22238653
Mean4540.556596
Median Absolute Deviation (MAD)2671
Skewness0.3320458831
Sum60611890
Variance10295764.98
MonotonicityNot monotonic
2022-04-11T14:12:39.442228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
17742
 
< 0.1%
17762
 
< 0.1%
17772
 
< 0.1%
17782
 
< 0.1%
17792
 
< 0.1%
17802
 
< 0.1%
17812
 
< 0.1%
17822
 
< 0.1%
17832
 
< 0.1%
Other values (10670)13329
99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
102
< 0.1%
ValueCountFrequency (%)
106821
< 0.1%
106811
< 0.1%
106801
< 0.1%
106791
< 0.1%
106781
< 0.1%
106771
< 0.1%
106761
< 0.1%
106751
< 0.1%
106741
< 0.1%
106731
< 0.1%

Airline
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.977526406
Minimum0
Maximum11
Zeros405
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:39.679391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q34
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.364158661
Coefficient of variation (CV)0.5943791239
Kurtosis0.3292713726
Mean3.977526406
Median Absolute Deviation (MAD)1
Skewness0.7207852624
Sum53096
Variance5.589246173
MonotonicityNot monotonic
2022-04-11T14:12:39.885834image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
44743
35.5%
32564
19.2%
12190
16.4%
61543
 
11.6%
81026
 
7.7%
10608
 
4.6%
0405
 
3.0%
2240
 
1.8%
716
 
0.1%
58
 
0.1%
Other values (2)6
 
< 0.1%
ValueCountFrequency (%)
0405
 
3.0%
12190
16.4%
2240
 
1.8%
32564
19.2%
44743
35.5%
58
 
0.1%
61543
 
11.6%
716
 
0.1%
81026
 
7.7%
91
 
< 0.1%
ValueCountFrequency (%)
115
 
< 0.1%
10608
 
4.6%
91
 
< 0.1%
81026
 
7.7%
716
 
0.1%
61543
 
11.6%
58
 
0.1%
44743
35.5%
32564
19.2%
2240
 
1.8%

Source
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.2 KiB
2
5679 
3
3581 
0
2752 
4
881 
1
 
456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row0
5th row3

Common Values

ValueCountFrequency (%)
25679
42.5%
33581
26.8%
02752
20.6%
4881
 
6.6%
1456
 
3.4%

Length

2022-04-11T14:12:40.093841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-11T14:12:40.373447image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
25679
42.5%
33581
26.8%
02752
20.6%
4881
 
6.6%
1456
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Destination
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.435313507
Minimum0
Maximum5
Zeros3581
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:40.554181image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.473504718
Coefficient of variation (CV)1.026608271
Kurtosis0.6480054839
Mean1.435313507
Median Absolute Deviation (MAD)1
Skewness1.248116424
Sum19160
Variance2.171216153
MonotonicityNot monotonic
2022-04-11T14:12:40.737234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
15679
42.5%
03581
26.8%
21582
 
11.9%
51170
 
8.8%
3881
 
6.6%
4456
 
3.4%
ValueCountFrequency (%)
03581
26.8%
15679
42.5%
21582
 
11.9%
3881
 
6.6%
4456
 
3.4%
51170
 
8.8%
ValueCountFrequency (%)
51170
 
8.8%
4456
 
3.4%
3881
 
6.6%
21582
 
11.9%
15679
42.5%
03581
26.8%

Duration
Categorical

HIGH CARDINALITY

Distinct373
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size821.5 KiB
2h 50m
 
672
1h 30m
 
493
2h 45m
 
432
2h 55m
 
418
2h 35m
 
399
Other values (368)
10935 

Length

Max length7
Median length6
Mean length6.004869279
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st row2h 50m
2nd row7h 25m
3rd row5h 25m
4th row4h 45m
5th row2h 25m

Common Values

ValueCountFrequency (%)
2h 50m672
 
5.0%
1h 30m493
 
3.7%
2h 45m432
 
3.2%
2h 55m418
 
3.1%
2h 35m399
 
3.0%
3h333
 
2.5%
2h 20m286
 
2.1%
2h 30m278
 
2.1%
2h 40m196
 
1.5%
2h 15m164
 
1.2%
Other values (363)9678
72.5%

Length

2022-04-11T14:12:40.964446image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2h2967
 
11.7%
30m1818
 
7.2%
20m1260
 
5.0%
50m1205
 
4.7%
45m1153
 
4.5%
35m1149
 
4.5%
15m1135
 
4.5%
55m1121
 
4.4%
25m1009
 
4.0%
40m803
 
3.2%
Other values (44)11796
46.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total_Stops
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size782.3 KiB
1.0
7055 
0.0
4340 
2.0
1895 
3.0
 
56
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.07055
52.9%
0.04340
32.5%
2.01895
 
14.2%
3.056
 
0.4%
4.02
 
< 0.1%
(Missing)1
 
< 0.1%

Length

2022-04-11T14:12:41.160631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-11T14:12:41.285622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
1.07055
52.9%
0.04340
32.5%
2.01895
 
14.2%
3.056
 
0.4%
4.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Additional_Info
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.407745899
Minimum0
Maximum9
Zeros20
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:41.512442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median8
Q38
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.198391911
Coefficient of variation (CV)0.1617755155
Kurtosis2.397648133
Mean7.407745899
Median Absolute Deviation (MAD)0
Skewness-1.784859053
Sum98886
Variance1.436143173
MonotonicityNot monotonic
2022-04-11T14:12:41.776418image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
810489
78.6%
52425
 
18.2%
7396
 
3.0%
020
 
0.1%
48
 
0.1%
35
 
< 0.1%
63
 
< 0.1%
11
 
< 0.1%
91
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
020
 
0.1%
11
 
< 0.1%
21
 
< 0.1%
35
 
< 0.1%
48
 
0.1%
52425
 
18.2%
63
 
< 0.1%
7396
 
3.0%
810489
78.6%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
810489
78.6%
7396
 
3.0%
63
 
< 0.1%
52425
 
18.2%
48
 
0.1%
35
 
< 0.1%
21
 
< 0.1%
11
 
< 0.1%
020
 
0.1%

Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1870
Distinct (%)17.5%
Missing2669
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean9085.449906
Minimum1759
Maximum79512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2022-04-11T14:12:42.160382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1759
5-th percentile3543
Q15277
median8372
Q312373
95-th percentile15764
Maximum79512
Range77753
Interquartile range (IQR)7096

Descriptive statistics

Standard deviation4610.904239
Coefficient of variation (CV)0.5075042278
Kurtosis13.3157598
Mean9085.449906
Median Absolute Deviation (MAD)3382
Skewness1.813937941
Sum97032605
Variance21260437.9
MonotonicityNot monotonic
2022-04-11T14:12:42.485447image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10262258
 
1.9%
10844212
 
1.6%
7229162
 
1.2%
4804160
 
1.2%
4823131
 
1.0%
14714109
 
0.8%
3943104
 
0.8%
1512993
 
0.7%
384191
 
0.7%
1289886
 
0.6%
Other values (1860)9274
69.5%
(Missing)2669
 
20.0%
ValueCountFrequency (%)
17594
 
< 0.1%
18401
 
< 0.1%
196536
0.3%
201735
0.3%
205010
 
0.1%
20716
 
< 0.1%
21757
 
0.1%
222740
0.3%
22289
 
0.1%
23856
 
< 0.1%
ValueCountFrequency (%)
795121
 
< 0.1%
624271
 
< 0.1%
572091
 
< 0.1%
548263
< 0.1%
522851
 
< 0.1%
522291
 
< 0.1%
464901
 
< 0.1%
369831
 
< 0.1%
362352
< 0.1%
351851
 
< 0.1%

Date
Real number (ℝ≥0)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.39036632
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:42.759506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median12
Q321
95-th percentile27
Maximum27
Range26
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.44003865
Coefficient of variation (CV)0.6303067779
Kurtosis-1.25973729
Mean13.39036632
Median Absolute Deviation (MAD)6
Skewness0.1349670303
Sum178748
Variance71.23425241
MonotonicityNot monotonic
2022-04-11T14:12:43.101066image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
91768
13.2%
61626
12.2%
211367
10.2%
271350
10.1%
11349
10.1%
241307
9.8%
151251
9.4%
121212
9.1%
31083
8.1%
181036
7.8%
ValueCountFrequency (%)
11349
10.1%
31083
8.1%
61626
12.2%
91768
13.2%
121212
9.1%
151251
9.4%
181036
7.8%
211367
10.2%
241307
9.8%
271350
10.1%
ValueCountFrequency (%)
271350
10.1%
241307
9.8%
211367
10.2%
181036
7.8%
151251
9.4%
121212
9.1%
91768
13.2%
61626
12.2%
31083
8.1%
11349
10.1%

Month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.2 KiB
5
4328 
6
4284 
3
3410 
4
1327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row3
5th row6

Common Values

ValueCountFrequency (%)
54328
32.4%
64284
32.1%
33410
25.5%
41327
 
9.9%

Length

2022-04-11T14:12:43.373039image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-11T14:12:43.709537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
54328
32.4%
64284
32.1%
33410
25.5%
41327
 
9.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.3 KiB
2019
13349 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
201913349
100.0%

Length

2022-04-11T14:12:43.929506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-11T14:12:44.070118image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
201913349
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Arrival_hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.39605963
Minimum0
Maximum23
Zeros411
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:44.179468image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median14
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.896702582
Coefficient of variation (CV)0.5148306869
Kurtosis-1.077425526
Mean13.39605963
Median Absolute Deviation (MAD)5
Skewness-0.3844995427
Sum178824
Variance47.5645065
MonotonicityNot monotonic
2022-04-11T14:12:44.478386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
192056
15.4%
121094
 
8.2%
41012
 
7.6%
21898
 
6.7%
22837
 
6.3%
1688
 
5.2%
18640
 
4.8%
23608
 
4.6%
8594
 
4.4%
10593
 
4.4%
Other values (14)4329
32.4%
ValueCountFrequency (%)
0411
3.1%
1688
5.2%
292
 
0.7%
361
 
0.5%
41012
7.6%
595
 
0.7%
664
 
0.5%
7518
3.9%
8594
4.4%
9591
4.4%
ValueCountFrequency (%)
23608
 
4.6%
22837
6.3%
21898
6.7%
20489
 
3.7%
192056
15.4%
18640
 
4.8%
17242
 
1.8%
16447
 
3.3%
15222
 
1.7%
14360
 
2.7%

Arrival_min
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.66064874
Minimum0
Maximum55
Zeros1827
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:44.681496image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q335
95-th percentile50
Maximum55
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.5570425
Coefficient of variation (CV)0.6713952529
Kurtosis-1.038536052
Mean24.66064874
Median Absolute Deviation (MAD)10
Skewness0.1117482913
Sum329195
Variance274.1356562
MonotonicityNot monotonic
2022-04-11T14:12:44.877469image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
01827
13.7%
151612
12.1%
251598
12.0%
351364
10.2%
201106
8.3%
301062
8.0%
50935
7.0%
45889
6.7%
5839
6.3%
40785
5.9%
Other values (2)1332
10.0%
ValueCountFrequency (%)
01827
13.7%
5839
6.3%
10717
 
5.4%
151612
12.1%
201106
8.3%
251598
12.0%
301062
8.0%
351364
10.2%
40785
5.9%
45889
6.7%
ValueCountFrequency (%)
55615
 
4.6%
50935
7.0%
45889
6.7%
40785
5.9%
351364
10.2%
301062
8.0%
251598
12.0%
201106
8.3%
151612
12.1%
10717
5.4%

Dept_hour
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.51277249
Minimum0
Maximum23
Zeros51
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:45.096203image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.736752484
Coefficient of variation (CV)0.4584717326
Kurtosis-1.197785085
Mean12.51277249
Median Absolute Deviation (MAD)5
Skewness0.1092454768
Sum167033
Variance32.91032906
MonotonicityNot monotonic
2022-04-11T14:12:45.419947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
91150
 
8.6%
71067
 
8.0%
8872
 
6.5%
6863
 
6.5%
17847
 
6.3%
20826
 
6.2%
5776
 
5.8%
11714
 
5.3%
19709
 
5.3%
10677
 
5.1%
Other values (14)4848
36.3%
ValueCountFrequency (%)
051
 
0.4%
144
 
0.3%
2228
 
1.7%
330
 
0.2%
4219
 
1.6%
5776
5.8%
6863
6.5%
71067
8.0%
8872
6.5%
91150
8.6%
ValueCountFrequency (%)
23189
 
1.4%
22486
3.6%
21625
4.7%
20826
6.2%
19709
5.3%
18553
4.1%
17847
6.3%
16602
4.5%
15431
3.2%
14647
4.8%

Dept_min
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.50333358
Minimum0
Maximum55
Zeros2590
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:45.675923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median25
Q340
95-th percentile55
Maximum55
Range55
Interquartile range (IQR)35

Descriptive statistics

Standard deviation18.8329269
Coefficient of variation (CV)0.7685863164
Kurtosis-1.304691908
Mean24.50333358
Median Absolute Deviation (MAD)20
Skewness0.1596940484
Sum327095
Variance354.6791356
MonotonicityNot monotonic
2022-04-11T14:12:45.867943image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02590
19.4%
301491
11.2%
551332
10.0%
451106
8.3%
101099
8.2%
5951
 
7.1%
15875
 
6.6%
25863
 
6.5%
20819
 
6.1%
35812
 
6.1%
Other values (2)1411
10.6%
ValueCountFrequency (%)
02590
19.4%
5951
 
7.1%
101099
8.2%
15875
 
6.6%
20819
 
6.1%
25863
 
6.5%
301491
11.2%
35812
 
6.1%
40646
 
4.8%
451106
8.3%
ValueCountFrequency (%)
551332
10.0%
50765
5.7%
451106
8.3%
40646
4.8%
35812
6.1%
301491
11.2%
25863
6.5%
20819
6.1%
15875
6.6%
101099
8.2%

duration_hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.23335081
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2022-04-11T14:12:46.150368image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q315
95-th percentile26
Maximum47
Range46
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.472646818
Coefficient of variation (CV)0.8279445289
Kurtosis-0.144615562
Mean10.23335081
Median Absolute Deviation (MAD)6
Skewness0.8580624796
Sum136605
Variance71.7857441
MonotonicityNot monotonic
2022-04-11T14:12:46.424434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
22967
22.2%
1785
 
5.9%
3627
 
4.7%
5608
 
4.6%
7600
 
4.5%
9551
 
4.1%
12538
 
4.0%
8531
 
4.0%
13516
 
3.9%
11467
 
3.5%
Other values (33)5159
38.6%
ValueCountFrequency (%)
1785
 
5.9%
22967
22.2%
3627
 
4.7%
4278
 
2.1%
5608
 
4.6%
6442
 
3.3%
7600
 
4.5%
8531
 
4.0%
9551
 
4.1%
10459
 
3.4%
ValueCountFrequency (%)
472
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
393
 
< 0.1%
3841
0.3%
3722
0.2%
3611
 
0.1%
3510
 
0.1%
349
 
0.1%

duration_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)0.1%
Missing1282
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean31.35990718
Minimum5
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2022-04-11T14:12:46.627525image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q120
median30
Q345
95-th percentile55
Maximum55
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.88542329
Coefficient of variation (CV)0.4746641373
Kurtosis-1.059999682
Mean31.35990718
Median Absolute Deviation (MAD)10
Skewness-0.03320228296
Sum378420
Variance221.5758265
MonotonicityNot monotonic
2022-04-11T14:12:46.815567image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
301818
13.6%
201260
9.4%
501205
9.0%
451153
8.6%
351149
8.6%
151135
8.5%
551121
8.4%
251009
7.6%
40803
6.0%
5767
5.7%
(Missing)1282
9.6%
ValueCountFrequency (%)
5767
5.7%
10647
 
4.8%
151135
8.5%
201260
9.4%
251009
7.6%
301818
13.6%
351149
8.6%
40803
6.0%
451153
8.6%
501205
9.0%
ValueCountFrequency (%)
551121
8.4%
501205
9.0%
451153
8.6%
40803
6.0%
351149
8.6%
301818
13.6%
251009
7.6%
201260
9.4%
151135
8.5%
10647
 
4.8%

Interactions

2022-04-11T14:12:32.439369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:54.743166image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:58.054264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:01.901909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:05.384809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:08.280007image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:12.087542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:15.846548image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:18.957901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:22.705674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:26.076007image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:29.006176image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:32.775337image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:55.103112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:58.414232image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:02.157886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:05.637587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:08.655967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:12.367538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:16.094505image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:19.176112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:22.933006image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:26.334551image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:29.442600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:33.111284image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:55.383082image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:58.838190image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:02.397884image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:05.849275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:09.015934image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:12.647491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:16.322749image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:19.434653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:23.151721image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:26.561781image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:29.786546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:33.463253image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:55.615067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:59.110163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:02.669841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:06.083630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:09.271909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:13.023457image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:16.542168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:19.845705image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:23.354831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:26.796142image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:30.074543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:33.807224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:55.863040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:59.526129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:02.917814image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:06.326607image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:09.627045image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:13.327435image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:16.785088image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:20.157678image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:23.582305image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:27.100827image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:30.370492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:34.089490image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:56.119014image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:59.838103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:03.301777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:06.748472image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:10.035011image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:13.569904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:17.085798image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:20.425016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:23.863519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:27.444799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:30.730460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:34.442107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:56.343015image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:00.078077image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:03.685740image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:06.960095image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:10.274377image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:13.816087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:17.297400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:20.774588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:24.164162image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:27.700775image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:31.010433image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:34.770097image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:56.566974image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:00.342054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:03.989734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:07.163189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:10.517635image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:14.480006image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:17.501306image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:21.078566image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:24.492131image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:27.906167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:31.250413image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:34.997837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:11:56.927384image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:00.606025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:04.230108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:07.390522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:10.858410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:14.775982image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:17.971854image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:21.376686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-04-11T14:12:04.752478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-04-11T14:12:25.388047image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-04-11T14:12:31.926435image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-04-11T14:11:57.774288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:01.477945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:05.096447image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:08.039606image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-04-11T14:12:28.787421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-11T14:12:32.145151image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-04-11T14:12:47.011271image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-11T14:12:47.379085image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-11T14:12:47.915017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-11T14:12:48.354978image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-11T14:12:48.859208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-11T14:12:36.400433image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-11T14:12:37.390183image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-11T14:12:38.037111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-11T14:12:38.319828image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexAirlineSourceDestinationDurationTotal_StopsAdditional_InfoPriceDateMonthYearArrival_hourArrival_minDept_hourDept_minduration_hourduration_min
003052h 50m0.083897.024320191102220250
111307h 25m2.087662.01520191315550725
233305h 25m1.086218.012520192330185525
343054h 45m1.0813302.013201921351650445
458302h 25m0.083873.02462019112590225
5640515h 30m1.0511087.01232019102518551530
6740521h 5m1.0822270.01320195580215
7840525h 30m1.0511087.0123201910258552530
896217h 50m1.088625.0275201919151125750
91012113h 15m1.088907.01620192309451315

Last rows

df_indexAirlineSourceDestinationDurationTotal_StopsAdditional_InfoPriceDateMonthYearArrival_hourArrival_minDept_hourDept_minduration_hourduration_min
13339266142133h 15m2.08NaN273201942519103315
1334026621431h 30m0.08NaN215201915251355130
1334126632308h 15m1.08NaN12520197452330815
13342266462110h 15m1.08NaN156201913015151015
1334326658431h 30m0.07NaN21620190152245130
13344266613023h 55m1.08NaN662019202520302355
1334526673302h 35m0.08NaN273201916551420235
1334626684216h 35m1.08NaN6320194252150635
13347266912115h 15m1.08NaN6320191915401515
13348267062114h 20m1.08NaN156201919154551420